# AI Bubble or Turning Point: What the Summer 2024 Market Correction Means for Companies

Author: Marin Niehues, Niehues Consulting · Published: 2024-08-08 · AI Strategy & Business Case
Source: https://www.niehuesconsulting.de/en/blog/ai-bubble-or-turning-point/

The share-price falls of summer 2024 say little about the benefit of AI inside your own company, and whether an initiative stands is decided by its own business case. Decision-makers should therefore stress-test their AI portfolio: does each initiative hold if supplier prices rise, a supplier drops out or expectations fall? Anyone who has answered these questions can stay calm through price swings in either direction and release investment in stages.

This article places the market correction of the first week of August in context, separates the question of how technology companies are valued from the question of what AI delivers in operations, and provides a stress test for your own portfolio. It is written for managing directors and heads of department who are being asked by boards, shareholders or colleagues whether their AI investments can still be justified after the price falls. The statements on markets are a classification and not investment advice.

**The valuation of technology groups and the benefit of an AI application in your own process are two separate questions. The business case answers the second, and a share price does not.**

## What happened on the markets in summer 2024?

On 5 August 2024 Japan's Nikkei 225 closed down 12.4 per cent, its largest one-day loss since the crash of October 1987. In the preceding week the US markets had already given ground. Reporting named weak US labour-market data, recession worries and the interest-rate rise by the Bank of Japan as triggers, the last of which accelerated the unwinding of debt-funded yen positions. Highly valued technology shares also fell in this phase. The following day the Nikkei recovered sharply.

A sober conclusion follows for companies. Prices reacted to interest rates, economic data and investor positioning, and there was no single cause in AI technology. Anyone who infers from the prices that AI does not work reads a market movement as a technical finding. Anyone who infers from the recovery that the worries are over makes the same mistake in the other direction.

## Is AI a bubble?

The question of a bubble concerns the price of assets, and what matters for companies is the separate question of what the technology delivers. The technology bubble around the turn of the millennium is the textbook case. The Nasdaq Composite reached its high in March 2000 and lost about 78 per cent by October 2002. The internet did not disappear as a result, usage kept growing, and companies that moved their processes onto the new technology in that period benefited for years. The economist Carlota Perez describes this pattern in "Technological Revolutions and Financial Capital" (2002): a phase of installation, in which capital pours into new technology and prices overshoot, is followed by a correction and then by a phase of broad application, in which the economic benefit arises.

Assessments of what AI delivers differ widely, and both sides have solid arguments. A field study of more than 5,000 customer support agents by Brynjolfsson, Li and Raymond, published in 2023 as an NBER working paper, found on average 14 per cent more issues resolved per hour, and considerably more for novices. The economist Daron Acemoglu estimated in spring 2024, also at the NBER, that AI will raise total factor productivity of the whole economy by no more than about 0.66 per cent over ten years, because only a share of tasks is affected. The two results do not contradict each other. They measure different levels: the effect in a clearly defined activity and the effect for the whole economy. For a company the first level counts, because it can be tested in its own process.

## What consequences can price swings have for companies?

Consequences arise mainly through the suppliers from which a company obtains AI. There are three routes: suppliers change prices and terms, a supplier discontinues a product or is acquired, and access to capital becomes scarcer for young suppliers. A company that ties its processes to a single supplier bears this risk in full. A company that keeps models and tools interchangeable takes price changes in its stride.

A second consequence concerns the mood inside the company. After price falls, shareholders and boards often ask for AI budgets to be frozen. After price gains they ask for the opposite. Both reflexes share the same flaw: they couple internal decisions to external prices. Initiatives with a proven benefit should continue, and initiatives without a business case should be stopped anyway. What budgets should look like beforehand is shown in the article on [AI investment without a plan](/en/blog/ai-investment-without-a-plan/), and how announcements can be separated from benefit in everyday work in the article on the [first half of 2024 in AI](/en/blog/ai-first-half-2024/).

## How do you run a stress test for the AI portfolio?

A stress test checks every running and planned initiative against four scenarios and assigns it a decision. The scenarios are: the supplier's prices rise sharply, the supplier discontinues the product, the expected benefit falls to half, and budgets are cut by a third. For each scenario you answer whether the initiative still has a positive business case and what is to be done. The scenarios and their thresholds (double the price, half the benefit, a third less budget) are assumptions of this article and not market figures. They can be adapted to your own portfolio.

**Stress test for the AI portfolio**
Scenario | Test question | If passed | If failed
Supplier price rises | Does the business case hold at double the price? | Continue the initiative | Change supplier or limit scope
Supplier drops out | Can the model or tool be replaced within a reasonable time? | Document the exit | Reduce dependence before expanding
Benefit falls to half | Does the business case stay positive? | Continue the initiative | Release in stages, measure benefit early
Budget is cut | Does operation of the first initiative run on less funding? | Keep the sequence | Fewer initiatives, each with full introduction

The test can be run in a half-day workshop if the business cases exist. If they do not, that is the first finding. An initiative without a proven benefit fails a stress test, because even under normal conditions it rests on assumptions. The basis of a business case is a clean measurement of the baseline before the start.

## How do you release AI investment in stages?

You release it in stages, each ending in a decision backed by measured figures. The first stage is a prototype in the real process with real data that shows the benefit on one metric. The second stage introduces the application in one area, with owners and adjusted processes. The third stage extends it to further areas. Before each stage leadership decides on the basis of the metric whether to go on. This staging protects against both errors: against the activism that starts everything at once out of fear of missing out, and against the freeze that stops even initiatives with a proven benefit after a price slide. The article on the [AI hype cycle](/en/blog/ai-hype-cycle-progress-versus-announcement/) deals with hype and progress, and the article on [AI washing](/en/blog/ai-washing-spot-products-claiming-ai/) with products that promise more than they deliver.

**Checklist for decision-makers**
- Does every AI initiative have a business case with a measured baseline?
- Does the business case hold at double the supplier price?
- Is it described how the initiative continues if the supplier changes?
- Do data and prompts belong to the company, and can they be exported?
- Does every initiative have an owner in the business unit?
- Is the next release tied to a metric?

## AI investment with Niehues Consulting

[Niehues Consulting](/en/) is a consultancy for AI management and organisational design from a delivery perspective, based in Düsseldorf. Founder Marin Niehues brings AI into operations, on projects from VC-funded scale-ups to DAX 40 groups and in over 20 countries on six continents. In the service area of AI management the work runs from business analysis and requirements through the business case to the selection and sequencing of applications and the steering of the portfolio.

The approach follows the Niehues Delivery Model. Three of its test questions matter for the question of bubble or turning point: What is the baseline, and how do we measure success? Is the business case confirmed with real values? Which metric shows after 90 days that the solution has landed? The entry point is a free initial consultation of 30 minutes, which you can arrange via [contact](/en/#kontakt). Scope and terms of an engagement are discussed there.

**Conclusion**
The market correction shows how fast expectations of technology companies can shift, and it says little about the benefit of AI in your own process. Anyone who runs each initiative with a business case, a stress test and staged release needs no forecast of share prices. The timing of investment then follows from the maturity of the application in your own operations.

**Sources**
- Nikkei Inc.: Closing price of the Nikkei 225 on 5 August 2024, down 12.4 per cent
- Nasdaq: Composite Index, closing prices March 2000 to October 2002, a fall of about 78 per cent
- Brynjolfsson, Li, Raymond: Generative AI at Work, NBER Working Paper 31161, 2023
- Acemoglu: The Simple Macroeconomics of AI, NBER Working Paper 32487, 2024
- Perez, Carlota: Technological Revolutions and Financial Capital, book, 2002

## Frequently asked questions

### Will the AI bubble burst?

Whether technology valuations are too high cannot be established in advance, and for companies the answer is not decisive. The bubble of 2000 shows that prices can fall by 78 per cent while the technology keeps spreading. For your initiative the business case counts.

### Should you stop AI investment after a stock market fall?

No, a market fall is no reason to stop initiatives with a proven benefit. Initiatives without a business case should be stopped, whatever the share price. The stress test in this article shows which initiatives hold under harder conditions.

### What was the market correction of August 2024?

On 5 August 2024 Japan's Nikkei 225 lost 12.4 per cent, its largest one-day loss since 1987. Reporting named weak US labour-market data, recession worries and the Bank of Japan's rate rise as triggers. Technology shares also fell.

### What does AI deliver according to documented studies?

A field study of more than 5,000 customer support agents found on average 14 per cent more issues resolved per hour. For the economy as a whole, the economist Daron Acemoglu estimates the effect on total factor productivity over ten years at no more than about 0.66 per cent. The two figures apply at different levels.

### How do you protect yourself against dependence on one AI supplier?

You keep models and tools interchangeable, store data and prompts in your own holdings and describe for each initiative the path if the supplier changes. A business case that holds at double the supplier price reduces the risk further.

### How do I justify AI investment to the board after a market crash?

With a business case for each initiative, a measured baseline and staged release. The argument then runs: each stage is released only after a measured metric, and the stress test shows which initiatives hold even at higher prices.

### When is the right time to invest in AI?

The right time has come when a use case has a business case with a measured baseline and the business unit can operate it. Share-price movements of the technology groups give no indication.

### What is staged release?

Staged release divides an AI initiative into prototype, introduction in one area and extension, and leadership decides before each stage on the basis of a metric. The capital deployed stays small while the benefit is unproven.